In recent years, an increasing number of researches have been focused on bio-inspired algorithms
to solve the elaborate engineering problems. Artificial Immune System (AIS) is an artificial intelligence
technique which has potential of solving problems in various fields. The immune system, due to self-regulating
nature, has been an inspiration source of unsupervised learning methods for pattern recognition task. The
purpose of this study is to apply the AIS to pre-process the lie-detection dataset to promote the recognition of
guilty and innocent subjects. A new Unsupervised AIS (UAIS) was proposed in this study as a pre-processing
method before classification. Then, we applied three different classifiers on pre-processed data for Event
Related Potential (ERP) assessment in a P300-based Guilty Knowledge Test (GKT). Experiment results showed
that UAIS is a successful pre-processing method which is able to improve the classification rate. In our
experiments, we observed that the classification accuracies for three different classifiers: K-Nearest
Neighbourhood (KNN), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) were
increased after applying UAIS pre-processing. Using of scattering criterion to assessment the features before
and after pre-processing proved that our proposed method was able to perform data mapping from a primary
feature space to a new area where the data separability was improved significantly.

S. Shojaeilangari and M.H. Moradi, 2012. A New Unsupervised Pre-processing Algorithm Based on Artificial Immune System for ERP Assessment in a P300-based GKT.
Research Journal of Applied Sciences, Engineering and Technology, 4(18): 3238-3245.